🤖 AI Summary
Sketch-based 3D reconstruction faces significant challenges due to the abstract, sparse nature of input sketches and their limited semantic and geometric information. To address this, we propose a two-stage generative framework that jointly leverages semantic bridging and symmetry constraints. First, a sketch-to-image translation module establishes cross-modal semantic alignment, mitigating information scarcity. Second, a symmetry-aware 3D reconstruction network incorporates reflection symmetry as a strong geometric prior to enhance structural plausibility and completeness. Evaluated on mainstream sketch datasets including ShapeNet, our method achieves state-of-the-art performance across all three core metrics—Chamfer Distance, Earth Mover’s Distance, and F-Score—demonstrating the effectiveness of co-modeling semantic guidance and geometric priors for sketch-based 3D reconstruction.
📝 Abstract
Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semantic and geometric information. To address this, we propose Sketch2Symm, a two-stage generation method that produces geometrically consistent 3D shapes from sketches. Our approach introduces semantic bridging via sketch-to-image translation to enrich sparse sketch representations, and incorporates symmetry constraints as geometric priors to leverage the structural regularity commonly found in everyday objects. Experiments on mainstream sketch datasets demonstrate that our method achieves superior performance compared to existing sketch-based reconstruction methods in terms of Chamfer Distance, Earth Mover's Distance, and F-Score, verifying the effectiveness of the proposed semantic bridging and symmetry-aware design.